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DRRN: Differential rectification & refinement network for ischemic infarct segmentation
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DOI：10.1049/cit2.12350.png)
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Accurate segmentation of infarct tissue in ischemic stroke is essential to determine the extent of injury and assess the risk and choose optimal treatment for this life-threatening disease. With the prior knowledge that asymmetric analysis of anatomical structures can provide discriminative information, plenty of symmetry-based approaches have emerged to detect abnormalities in brain images. However, the inevitable non-pathological noise has not been fully alleviated and weakened, leading to unsatisfactory results. A novel differential rectification and refinement network (DRRN) for the automatic segmentation of ischemic strokes is proposed. Specifically, a differential feature perception encoder (DFPE) is developed to fully exploit and propagate the bilateral quasi-symmetry of healthy brains. In DFPE, an erasure-rectification (ER) module is devised to rectify pseudo-lesion features caused by non-pathological noise through utilising discriminant features within the symmetric neighbourhood of the original image. And a differential-attention (DA) mechanism is also integrated to fully perceive the differences in cross-axial features and estimate the similarity of long-range spatial context information. In addition, a crisscross differential feature reinforce module embedded with multiple boundary enhancement attention modules is designed to effectively integrate multi-scale features and refine textual details and margins of the infarct area. Experimental results on the public ATLAS and Kaggle dataset demonstrate the effectiveness of DRRN over state-of-the-arts.
Keyword:
computer vision
deep neural networks
image segmentation
medical image processing
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663
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